CHAPTER III DATA AND METHODOLOGY

Transcription

1 CHAPTER III DATA AND METHODOLOGY

2 DATA AND METHODOLOGY 3.1 Study area Nuranang river basin (Fig. 3.1) located at Tawang district of Arunachal Pradesh, India with an area of 52 km 2 was selected for the present study. Topography of the Tawang district is almost covered with the Himalayan range and its altitude varies from 1829 m to 6706 m above mean sea level (MSL). Most part of the district is existent at higher mountain range which receives snowfall. The geographical area is approximately 2,172 km 2. The district is situated between 27 22' to 27 45' N Latitudes and 90 45' to 92 15' E Longitudes. Tawang district is surrounded by Tibet in the north, Bhutan to the southwest and Sela ranges separated from West Kameng District of Arunachal Pradesh in the east. The Sela range includes a series of mountains arranged in the form of a big line from Tibet in the north to Bhutan in the west and thus forming a tough terrain to pass through. The altitude of Sela range varies from 4267 m to 4572 m and Sela pass is at 4180 m. The river system of the district is a part of the Brahmaputra river basin and the main rivers are Tawang chu and Nyamjang chu. Both the rivers are tributaries of the Manas river. The drainage pattern is generally dendritic to sub-parallel in nature and flow the geomorphological trends of the hills and mountains. The average annual rainfall is 1828 mm and the mean maximum and minimum temperature is 25 C and -4 C, respectively. Nuranang river, a tributary to Tawang river, is originated from Sela Lake and joins Tawang river as Nuranang fall at Jang (Fig. 3.2). The altitude of the Sela Lake is 4211 m above MSL and it lies at 27 30' 09" N and 92 06' 17" E. The Central Water Commission (CWC) discharge site at Jang is selected as the outlet point and it lies at 27 33' 01" N and 92 01' 13" E, with an elevation of 3474 m above MSL (Fig. 3.3). Elevation of the basin ranges from 3143 m to 4946 m above MSL with an average slope of 51%. At upper elevation, climate is alpine and at lower elevation, it is temperate. Latitude ranges from 27 30' N to 27 35' N, whereas longitude is in between 92 E to 92 7' E. Monsoon season starts from May to September having an average annual precipitation of 1139 mm. The entire river

3 Fig. 3.1 Nuranang river basin in Tawang district of Arunachal Pradesh, India. Nuranang falls Hydro-power plant Tawang river Fig. 3.2 Nuranang Fall at Jang, Tawang Data and Methodology 37

4 Fig. 3.3 Central Water Commission (CWC) gauging site at Jang Data and Methodology 38

5 basin is dominated by seasonal snow. Snowfall starts from late October and ends at March. Melting of snow starts at February and completely depletes in early June. Snow accumulation and ablation periods vary with years Description of installation site Automatic Weather Station (AWS) and Snow Water Equivalent Recorder (SWER) are installed at Sela Top (Fig. 3.4) inside the Indian Military Based Camp (Communication DET) near the Sela Lake, Tawang District in Arunachal Pradesh, India. The location of the installation site is 27 30' 09" N Latitude and 92 06' 17" E Longitude at an altitude of 4211 m above MSL. The station site is uninhabited and Rocky Mountains. SWER and AWS have been installed on 30 th October 2011 and 2 nd November 2012, respectively. 3.2 Acquisition of data Meteorological and hydrological data The meteorological and hydrological data from (Table 3.1) measured at CWC discharge site at Jang, Tawang district were collected from CWC office, Itanagar, Arunachal Pradesh, India. The long term average temperature and precipitation data for (Figs. 3.5 and 3.6) show that, in the month of February, the temperature is lowest and then increasing till the mid of August, while the precipitation attains peak in the month of July. These two parameters have a great impact on the rate of depletion of snow Satellite data Satellite images (LISS-III/AWiFS) for the five block years ( , , , , and ) of snow accumulation and depletion period (October May) were procured from National Remote Sensing Centre (NRSC), Indian Space Research Organisation (ISRO), Hyderabad, India (Table 3.2). Shuttle Radar Topography Mission (SRTM) with resolution 90 m 90 m digital elevation model (DEM) was downloaded for the study area from (Fig. 3.7). Data and Methodology 39

6 Fig. 3.4 Automatic Weather Station (AWS) and Snow Water Equivalent Recorder (SWER) at Sela top Table 3.1 Hydro-meteorological data Sl. No. 1. Type Daily Maximum and Minimum Temperature From Years Upto Daily Rainfall Source CWC, Itanagar 3. Daily Discharge Data and Methodology 40

7 Precipitation (mm) Temperature ( o C) Oct Nov Dec Jan Feb Mar Apr May Jun July Aug Sep Month Fig. 3.5 Long term average monthly temperatures for year Oct Nov Dec Jan Feb Mar Apr May Jun July Aug Sep Month Fig. 3.6 Long term average monthly precipitation for year Data and Methodology 41

8 Table 3.2 Satellite images Sl. No. Snow block year Satellite image 1. October 2005 to May images 2. October 2006 to May images 3. October 2008 to May images 4. October 2009 to May images 5. October 2010 to May images Fig. 3.7 Digital elevation model (DEM) with resolution of 90 m 90 m Data and Methodology 42

9 3.2.3 Climatic scenarios For this study, projected temperature and precipitation data were downloaded as shapefiles with regular grid points from NCAR's GIS Program Climate Change Scenarios GIS data portal ( for different emission scenarios (SRES; Nakicenovic et al., 2003, Girod et al., 2009), viz., A1B, A2, B1; and IPCC commitment scenario (non-sres) for different future years (2020, 2030, 2040, and 2050) (CCSM3; Collins et al., 2006). NCAR is managed by University Corporation for Atmospheric Research (UCAR). This portal is intended to serve a community of GIS users interested in climate change. The free datasets of climate change projections can be downloaded as a shapefile, a text file, or as an image. These climate change projections were generated by the NCAR CCSM as per the SRES given by AR4 of the IPCC. The CCSM allows researcher to study the earth s past, present and future climate states. CCSM3 was released in June The resolution of climate change datasets generated from the CCSM3 runs in approximately degrees and approximate spatial resolution of global climate projections is 155 km. Pursuing climate information from GCMs projection required downscaling to obtain information known at large scale to make predictions at the regional scale. The projected temperature and precipitation data at CWC Jang site were obtained by spatially interpolating the gridded data using inverse distance weighting (IDW) and then by statistical downscaling using linear regression models developed between past 10 years ( ) CCSM3 modelled data and observed data at CWC Jang (Swain et al., 2014). On comparison, it was found that the CCSM3 model underestimated observed precipitation and slightly overestimated observed temperature for years , which was rectified in the projected data of future years using linear regression models developed for past data. The downscaled monthly average temperature and precipitation data of projected climatic scenarios for different future years were compared with 10 years ( ) average temperature and precipitation on monthly basis as obtained from CWC, Jang (Figs. 3.5 and 3.6). Table 3.3, and Figs. 3.8 and 3.9 show the change in precipitation in percentage and temperature in degree Celsius under projected climatic scenarios for different future years. Data and Methodology 43

10 Increase in Precipitation (%) Table 3.3 Rate of change of precipitation and temperature under projected climatic scenarios for future years Year Scenario PPT (%) TEMP ( C) PPT (%) TEMP ( C) PPT (%) TEMP ( C) PPT (%) TEMP ( C) A1B A B IPCC Commitment Note: (-ve) indicates decreases and (+ve) indicates increases A1B Projected Precipitation A2 20 B1 15 IPCC Commitment Year Fig. 3.8 Projected precipitation for the future years Data and Methodology 44

11 Increase in Temperature ( C) A1B Projected Temperature 1.40 A B IPCC Commitment Year Fig 3.9 Projected temperatures for the future years Data and Methodology 45

12 3.3 Generation of hypsometric curve for Nuranang basin Nuranang basin was divided into three elevation zones viz., Zone 1, Zone 2, and Zone 3. The total elevation of the study site ranges from 3143 m to 4964 m. The elevation range of the sub-basin is exceeding 500 m so it was sub-divided into elevation zones of about 601 m each. Since the SRM model is sensitive to lapse rate and also CWC station is located at low elevation (3474 m), so lapse rate was used to compute mean daily temperature at each zone s hypsometric mean elevation (4215 m). Fig shows the DEM for three elevation zones as well as whole basin. Area elevation curve or hypsometric curve for the whole basin (Fig. 3.11) was derived from DEM using ArcGIS 10. Area elevation curve or hypsometric curve is the plotted graph of total basin area against the elevation range. From this curve, one can determine the area of the basin below a particular elevation. The mean hypsometric elevations for each elevation zone and whole basin were determined from the area elevation curve (Table 3.4). The hypsometric mean elevation (metres) is the elevation within each zone where total zone area below this elevation equals the total zone area above the same point. Area under each elevation zones are also mentioned in Table Snow cover mapping SCA% is referred as the areal extent of snow covered ground which is usually expressed as percentage of total area in a given region. The snow water equivalent (SWE) of the snowpack is important for hydrological modelling and runoff prediction. Snowmelt plays a major role in seasonal energy exchanges between the atmosphere and ground, affecting soil moisture and runoff, and thereby water resources. SCA is important in hydro-climatic models and in monitoring climate change. The SCA percentages can be estimated using GIS software: ERDAS and ArcGIS. In Himalayas, cloud cover is quite common. In the visible portion of the electromagnetic spectrum, snow and cloud both appear bright white and create confusion in SCA estimation. Mountain shadow also makes it difficult for the discrimination of SCA under mountain shadow from snow free areas. So, in the present study, NDSI model has been developed to overcome the above difficulties. NDSI model can differentiate snow from clouds and also map the snow in mountain shadows. Data and Methodology 46

13 Elevation (m) a) Whole basin ( m) b) Zone 1 ( m) c) Zone 2 ( m) d) Zone 3 ( m) Fig Digital elevation model (DEM) of three elevation zones for Nuranang basin Whole basin (4215 m) Zone 3 (4480 m) Zone 2 (4150 m) Zone 1 (3601 m) Area (sq. km) Fig Whole basin with zonal mean hypsometric elevation Data and Methodology 47

14 Table 3.4 Mean hypsometric elevation and area for different elevation zones Elevation range Mean hypsometric Area Zone (m) elevation (m) (sq. km) Zone Zone Zone Whole basin Data and Methodology 48

15 3.4.1 Development of NDSI model The NDSI is useful for the identification of snow as well as for separating snow from clouds. NDSI uses the high and low reflectance of snow in visible (Green) and shortwave infrared (SWIR) bands, respectively and it can also delineate and map the snow in mountain shadows (Kulkarni et al., 2002). Additionally, the reflectance of clouds remains high in SWIR band, thus NDSI allows discrimination of clouds from snow. NDSI ranges from -1 to +1. The NDSI can be estimated using the following equation (Hall et al., 2002, Kulkarni et al., 2002): ( ) ( ) ( ) ( ) ( ) Estimation of NDSI requires reflectance value which can be determined from digital number (DN) of the input satellite image. Kulkarni et al. (2006) showed that the NDSI value can be effectively used to map the aerial extent of snow cover from AWiFS scenes if an appropriate threshold value can be chosen to differentiate SCA from non-snow areas. They suggested that all snow cover areas, including snow in forest and in mountain shadow, can be mapped if the threshold value is kept at 0.4. Differentiation between water and snow is difficult using an NDSI image. However water pixels can be removed by masking the water bodies marked on pre-winter images (Fig. 3.12). In the present study, an NDSI model was coded in ERDAS using Model Maker tool for discrimination of snow from the satellite imagery data for the study area. The developed NDSI model was based on Kulkarni et al., The flowchart of the developed model is shown in Fig First, the geo-rectified multi-spectral satellite images (LISS-III/AWiFS) were used as the input file to the NDSI model. Then, the band 2 and band 5 images were separated from the input image. The images obtained by 10 bit sensor were rescaled to eight bit float images. Rescaling of DN values was done by using the following equation: ( ) Data and Methodology 49

16 Fig Flowchart for separation of snow and water pixels (Kulkarni et al., 2006) Data and Methodology 50

17 Input image B2 Rescaling from 10bit to 8bit B5 Rescaling from 10bit to 8bit Float image B2 Float image B5 DN to Radiance value conversion DN to Radiance value conversion Float image B2 Float image B5 Estimation of reflectance Estimation of reflectance Float image B2 Float image B5 (B2-B5)/ (B2+B5) NDSI image Differentiating snow from cloud Snow + Water NDSI > 0.4 Fig NDSI model Data and Methodology 51

18 where, = Digital number from 8 bit sensor and = Digital number from 10 bit sensor. Next the rescaled DN values for each band were converted into radiance value by using the equation as below (Negi et al., 2009): ( ) ( ) where, = Spectral radiance at sensor aperture in the band (mw cm -2 sr -1 µm -1 ) = Spectral radiance in the band at DN max, (mw cm -2 sr -1 µm -1 ) = Spectral radiance in the band at DN min, (mw cm -2 sr -1 µm -1 ) = Digital number of pixel in the band = Highest DN value in scale Further, the radiance values were converted into reflectance using the following equation (Negi et al., 2009): ( ) where, = Reflectance in the band = Earth-Sun distance (Astronomical units, AU) = Mean solar exo-atmospheric spectral irradiance (mw cm -2 µm -1 ) = Solar zenith angle (degrees) The values of and & are presented in Table 3.5 ( usgs.gov/documents/irsp6.pdf and The distance ( ) is the distance between earth and sun for that particular day on which satellite sensors take image of the study region and it can be obtain from the Data and Methodology 52

19 Table 3.5 Value of and & Sensors Band (mw cm -2 µm -1 ) (mw cm -2 sr -1 µm -1 ) (mw cm -2 sr -1 µm -1 ) IRS P6 AWiFS Band Band IRS P6 LISS-III Band Band IRS 1C LISS-III Band Band Data and Methodology 53

20 website ( The solar Zenith angle ( ) can be obtained from equations given in pdf_atms360/ solareqns.pdf. Finally the NDSI image was obtained by executing Eqn NDSI values greater than 0.4 were considered as snow plus water. The SCA of the study area were estimated using ArcGIS software for the whole basin ( m), Zone 1 ( m), Zone 2 ( m), and Zone 3 ( m) by masking the DEMs of the corresponding zone in the NDSI images Validation of NDSI model The present NDSI model was validated using Moderate Resolution Imaging Spectroradiometer (MODIS) fractional snow cover products. MODIS is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra s orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth s surface every one to two days, acquiring data in 36 spectral bands, or groups of wavelengths. These data improve the understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models to predict global change accurately enough and to assist policy-makers in making sound decisions concerning the protection of global environment. Three snow block year of , , and of MODIS daily snow cover products were downloaded from ( The Nuranang basin comes under the tile identifier h26v06 i.e., horizontal 26 and vertical 6. These data are in HDFEOS format and gridded in a sinusoidal map projection. This sinusoidal projection was re-projected to Geographic Lat/Long and WGS84 datum. All the MODIS data products were found to be very accurately geo-referenced (Jain et al., 2008). Fractional snow cover (FSC) mapping achieve a more precise estimate of areal snow cover extent by estimating the fraction of a pixel that is snow covered. The most common snow fraction methods applied to MODIS images were spectral Data and Methodology 54

21 un-mixing and an empirical NDSI. Table 3.6 shows the outlines of implication of pixel values for MODIS. MODIS (both Terra and Aqua) Snow Cover Daily L3 Global 500 m Grid (MOD10A1) contains snow cover and quality assurance (QA) data in HDF-EOS format along with corresponding metadata. MODIS Re-projection Tool (MRT) (version 4.1) was used to convert the HDF-EOS to GeoTIFF. MRT enables users to read data files in HDF-EOS format (MODIS Level-2G, Level-3, and Level-4 land data products), specify a geographic subset or specific science data sets as input to processing, perform geographic transformation to a different coordinate system/cartographic projection, and write the output to file formats other than HDF- EOS. The MRT was downloaded from the site For the three block years ( , , and ) from October to May, the SCA% from MODIS were determined for the same dates of which IRS-P6 images were procured. SCA from the daily selected MODIS image was calculated by obtaining total number of pixel under different land cover types. SCA% and cloud cover area percentage from daily image were calculated from the total number of pixel corresponding to snow and cloud, respectively as below: ( ) ( ) where, SCA% = snow covered area percentage and CCA% = cloud covered area percentage. 3.5 Model setup In this study, for snowmelt runoff modelling, Snowmelt Runoff Model (SRM) (Martinec et al., 2008) was selected. The main characteristic of the SRM model is that it can be used effectively in mountain region with limited data availability and Data and Methodology 55

22 Table 3.6 Fractional snow cover field coded integer values Value Description fractional snow 200 missing data 201 no decision 211 night 225 land 237 inland water 239 ocean 250 cloud 254 detector saturated 255 fill Data and Methodology 56

23 it is relatively simple. In addition with the advance of satellite remote sensing, snow covered mapping is possible particularly the high inaccessible mountains, which is a main input variable of the model. The SRM model also successfully underwent tests by the World Meteorological Organization (WMO) in regard to runoff simulations (WMO, 1986) and to partially simulated conditions of real time runoff forecasts (WMO, 1992). At start, the model can be run with a known or estimated discharge value for an unlimited number of days, as long as the input variables temperature, precipitation and SCA are provided (Martinec and Rango, 1986). The present study site is at higher elevation and dominated by seasonal snow. The region is tortuous mountain system with varying elevation providing limited network of hydrometeorological stations. Taking account of the possible data availability and requirement of model basic inputs justified the application of SRM model in this region Governing equation of WinSRM The SRM (Martinec et al., 2008) is designed to simulate and forecast daily streamflow in mountain basins. It can also be applied to evaluate the effect of a changed climate on seasonal snow cover and runoff. The model can be applied to any size of mountain basins (so far from 0.76 to 917,444 km 2 ) and any elevation range (basin can be divide into 16 elevation zones). SRM is based on Windows platform (WinSRM) and flow chart of the model is given in Fig The runoff produced from the snowmelt and rainfall can be computed on daily basis, superimposed on the calculated recession flow and transformed into daily discharge from the basin as below: [ ( ) ] ( ) ( ) where, Q = average daily discharge (m 3 s -1 ); c = runoff coefficient expressing the losses as a ratio (runoff/precipitation), with c S referring to snowmelt and c R to rain; a = degree-day factor (cm C -1 d -1 ) indicating the snowmelt depth resulting from one degree-day; T = number of degree-days ( C d); ΔT= the adjustment by temperature lapse rate when extrapolating the temperature from the station to the Data and Methodology 57

24 Temperature Snow and glacier covered area Precipitation Distribution of temperature Snowmelt Contributing Area Distribution of snow Snow Form of precipitatio n Rain Snow/Glacier melt runoff Rain-induced runoff Distribution of rain Snow/Glacier melt runoff + Rain-induced runoff (present day) Recession flow from previous day Daily total runoff at outlet Fig Flow chart of WinSRM (Singh and Bengtsson, 2005) Data and Methodology 58

25 average hypsometric elevation of the basin or zone ( C d); S = ratio of the snow covered area to the total area; P = precipitation contributing to runoff (cm). A preselected threshold temperature, T CRIT, determines whether this contribution is rainfall and immediate. If precipitation is determined by T CRIT to be new snow, it is kept on storage over the hitherto snow free area until melting conditions occur; A = area of the basin or zone (km 2 ); k = recession coefficient indicating the decline of discharge in a period without snowmelt or rainfall: (m, m + 1 are the sequence of days during a true recession flow period); n = sequence of days during the discharge computation period Input variables of WinSRM The model requires three basic daily values of the input variables viz., temperature, precipitation, and SCA% derived from remote sensing observations Temperature and degree-days, T The model expressed the number of degree-days in the form of temperature. The average of daily maximum and minimum temperature data as collected from CWC observation station at Jang were determined. The average temperature was used to compute the daily number of degree-days responsible for snowmelt. ( ) where, = average number of degree-days; = maximum temperature; = minimum temperature. Average number of degree-days as obtained from CWC observation site was adjusted up to hypsometric mean elevation of any zone by using altitude adjustment as below: ( ) Data and Methodology 59

26 The altitude adjustment, T was computed as ( ) ( ) where, = temperature lapse rate ( C per 100 m); = altitude of the temperature measuring station (m); = hypsometric mean elevation of a zone (m). The calculation of temperature lapse rate for Arunachal Pradesh is discussed under Art Precipitation, P Precipitation on daily basis was collected from CWC observation site at Jang. At high altitude, a critical temperature is used to decide whether a precipitation event will be treated as rain or new snow. The precipitation is decided to be rain, when temperature is higher than the critical temperature otherwise the precipitation is to be snow. Selected value of critical temperature for Nuranang basin is provided under Art The new snow that falls over the previously SCA is assumed to become part of the seasonal pack and its effect is included in the normal depletion curve of the snow coverage. The new snow falling over the snow-free area is considered as precipitation to be added to snowmelt, with this effect delayed until the next day warm enough to produce melting Snow covered area percentage, S Multi-spectral satellite images (LISS-III/AWiFS) were used to determine the SCA%. In WinSRM, depletion curve of the snow coverage is an important input variable. Daily SCA% was determined by interpolating periodical snow cover percentages as obtained from procured monthly images. SCA is a typical feature of mountain basins and the areal extend of the seasonal snow cover gradually decreases during snow melt season Parameters of WinSRM Seven parameters were involved to run the SRM model, viz., runoff coefficients, degree-day factor, temperature lapse rate, critical temperature, rainfall contribution area, recession coefficient, and time lag. Data and Methodology 60

27 Runoff coefficient, c Runoff coefficient accounts the runoff losses, i.e. the difference between the available water volume (snowmelt + rainfall) and the outflow from the basin. The runoff coefficient is different for snow and rain. The model can accept separate runoff coefficient for snow (c S ) and rain (c R ). The runoff coefficients were calibrated in the present study. The calibration ranges of runoff coefficients for snow and rain were taken as 0.1 to Degree-day factor, a The degree-day factor converts the number of degree-days into the daily snowmelt depth as follows: ( ) where, M = daily snowmelt depth (cm); a = degree-day factor (cm C d -1 ); and T = number of degree-days ( C d). Degree-day factor (a) is daily decrease in snow water equivalent per degree increase in degree-days (Martinec, 1960). Average of maximum and minimum temperatures of a particular day refers to degree-days (T) of that particular day. Whenever the degree-day number becomes negative, it is set to zero so that no negative snowmelt is computed. To obtain daily snow water equivalent, SWER was installed at hypsometric elevation of whole basin (4215 m) at Sela Top of Tawang district of Arunachal Pradesh (27 30' 09" N, 92 06' 17" E). For one snow accumulation and ablation period of the Nuranang basin, average degree-day factor was obtained as 0.3 cm C -1. This average value matches quite well with the average of recommended monthly values as given in Table 3.7 (WMO, 1964). SWE is the snow water content of snow pack. SWE of new snow is less and it increases when snow becomes ripen. In a result, degree-day factor increases with advancement of melting season. So, degree-day factor should not be constant throughout the melting season. Different values for different ablation months were used as per WMO recommendation (WMO, 1964). Data and Methodology 61

28 Table 3.7 Proposed degree-day factor (a) cm/ C for partial forest cover (WMO, 1964) Month February 0.1 March 0.1 April 0.3 May 0.4 June 0.6 Degree-day factor Data and Methodology 62

29 Temperature lapse rate, Since temperature variation is major factor for melting of snow/ice at high mountain peaks, so lapse rate is an important parameter in hydrological model such as SRM to determine the temperature variation with elevation. The lapse rate can be predetermined from historical data if temperature stations at different altitudes are available. Otherwise it must be evaluated by analogy from other basins or with regard to climatic conditions. The temperature lapse rate is the change in temperature with altitude. It is used to adjust temperature measured at the basin reference elevation to each zone s hypsometric mean elevation. For the present study, the decrease in air temperature with increase in altitude was estimated by simple linear regression for maximum, minimum and mean monthly temperatures with altitudes. Hourly temperature data of 18 AWS stations of Arunachal Pradesh for the period of five years ( ) were downloaded from MOSDAC ( (Table 3.8). Due to limited data availability, only topographic descriptions were available so the simple regression analysis technique was being used (Douguedroit and desaintignon, 1970). In this study, 16 sites were used to analyse the temperature lapse rate, as the observed data conditions at both the stations of North Eastern Electric Power Corporation Limited (NEEPCO) were miserable and unusable, hence these two stations were excluded. A linear regression equation was used to calculate a series of linear equations for maximum, minimum and mean temperatures for different months as below (Rolland, 2003): ( ) ( ) where, T ij = air temperature ( C) modelled by the equation; = lapse rate (for temperature parameter i, and month j) ( C m -1 ); B ij = temperature at sea level ( C at zero m altitude); Altitude = elevation above sea level, m; i = type of temperature index (maximum, minimum, or mean temperature); j = month index (from Jan to Dec and whole year; 12+1 cases). Data and Methodology 63

30 Table 3.8 Automatic Weather Stations (AWS), Arunachal Pradesh Sl. No. Station Name Latitude ( N) Longitude ( E) Altitude (m) Data Starts Data Ends 1 Anini Basar Bomdila Daporijo Itanagar Jang Khonsa Koloriang Namsai Pasighat Roing Seppa Tawang Teju Yingkiong Ziro NEEPCO (Mengio) NEEPCO (Lower Subansiri) Data and Methodology 64

31 Performance Criteria The r 2 was estimated using the following equation: ( )( ) ( ) [ ( ) ( ) ] where, n = number of observations; = temperature and = average temperature. For a perfect correlation, the value of r 2 is 1.0, i.e., when the temperature values correlate perfectly with the altitude. A lower value (close to zero) of r 2 indicates poor correlation. Annual linear regression models for maximum, minimum and mean temperature are shown in Fig From the results of linear regression analysis (Fig. 3.16), it has been observed that monthly lapse rate varies from , , and C/100 m for maximum, minimum, and mean temperatures, respectively. The annual lapse rate values are 0.464, 0.476, and C/100 m for maximum, minimum and mean temperatures, respectively (Table 3.9). It can be seen that the annual lapse rate for minimum temperature is highest and mean temperature being the lowest. From linear regression results, an average value of 0.5 C/100 m as lapse rate for mean air temperature was considered for the model simulation. Linear regression model was validated for Sela top station (Fig. 3.4) from the period of November 2012 to April 2013 by comparing the temperatures computed by the model and temperature measured by AWS at the station. The r 2 for maximum, minimum and mean temperature was 0.56, 0.62, and 0.81, respectively. The results show that the performance of the regression models is satisfactory and can be adopted for Arunachal Himalaya Critical temperature, T CRIT The critical temperature is pre-selected value of temperature which determines whether the precipitation event is rain or snow. In WinSRM, the critical temperature Data and Methodology 65

32 (a) Linear regression model for maximum temperature on annual basis (b) Linear regression model for minimum temperature on annual basis Fig Linear regression model for maximum, minimum, and mean temperature on annual basis (contd.) Data and Methodology 66

33 Lapse rate ( C/100m) (c) Linear regression model for mean temperature on annual basis Fig Linear regression model for maximum, minimum, and mean temperature on annual basis (concld.) 0.60 Seasonal variation in lapse rates Max. ( C/100m) Min. ( C/100m) Mean ( C/100m) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Fig Lapse rates for maximum, minimum, and mean temperatures Data and Methodology 67

34 Table 3.9 Monthly variation of lapse rate for maximum, minimum, and mean temperatures Month Max. ( C/100m) Min. ( C/100m) Mean ( C/100m) January February March April May June July August September October November December Annual Data and Methodology 68

35 is used only in the snow melt season in order to decide whether precipitation is immediately contributes to runoff (if rain), or having delayed contribution (if snow). Model keeps the newly fallen snow in storage until it is melted on subsequent warm days. Generally, critical temperature is kept higher than the freezing point and diminished to 0 C. Critical temperature was calibrated for the basin in the range of C for all calibration years Rainfall contribution area, RCA Rainfall contribution area helps to determine whether the rainfall induced runoff is added to snowmelt induced runoff only from the snow-free area or from the entire zone area. For RCA = 0, it is assumed that rain falling on the snowpack early in the snowmelt season is retained by the snow which is usually dry and deep, and rainfall contributes to runoff only from the snow-free area. At some later stage, the snow cover becomes ripe (RCA = 1) and if rain falls on this snow cover, it is assumed that the same amount of water is released from the snowpack so that rain from the entire zone area contributes to runoff. RCA equals to 0 or 1 or combination of 0 and 1 were tried in this study Recession coefficient, k The recession coefficient is an important parameter of SRM since (1-k) is the proportion of the daily melt water production which immediately appears in the runoff. Analysis of historical discharge data is usually a good way to determine k. The historical discharge values of Nuranang river at CWC gauging site (Jang), Q n and Q n+1 were plotted against each other (Fig. 3.17) and the lower envelope line of all points was considered to indicate the k-values as below: ( ) From historical discharge dataset it was obtained that when Q 1 = 5 m 3 s -1, k 1 = and when Q 2 = 25 m 3 s -1, k 2 = Data and Methodology 69

36 Qn+1 (m 3 /s) 30.0 Recession Coefficient Qn (m 3 /s) Fig Recession flow plot Q n vs. Q n+1 for the Nuranang basin Data and Methodology 70

37 This means that k is not constant, but decreases with the increasing Q according to the equation: ( ) By solving the equation (3.14), the constants x and y were obtained as 0.61 and respectively Time lag, L Time lag indicates the time which the rising of discharge lags behind the rise of temperature (Martinec et al., 2008). For the present study area having a small size basin and steep slope, a lag time of 2, 4, 6, 8, 10, and 12 hours were tried for calibration Baseflow separation Baseflow is a component of the total streamflow that contribute as groundwater continuously to the streamflow. Estimation of baseflow helps in accessing the potential water supplies of the stream. In this study the baseflow have been separated from the observed total streamflow and direct runoff was used to compare with the WinSRM model predicted runoff (Fig. 3.18). The algorithm used to separate the baseflow is given below (Hughes et al., 2003; Welderufael and Woyessa, 2010); ( )( ) ( ) ( ) ( ) where, QT i = total flow times series; q i = high-flow time series component; QB i = baseflow time series component; DRH = direct runoff; i = time step index; α,β = separation parameters (0<α<1, 0<β<0.5) The values of α and β were taken as and 0.5, respectively. Data and Methodology 71

38 Streamflow (cumec) QTi QBi DRH Month ( ) Fig Baseflow separation Data and Methodology 72

39 3.5.5 Performance indicators To evaluate the performance of WinSRM, predicted discharges were compared with the observed ones. The performance can be visually interpreted by plotting the simulated and observed data simultaneously on a single plot. However, statistical tests can give the quantitative performance of the prediction. Here, two dimensionless statistical performance criteria viz., Modelling Efficiency (ME) and Coefficient of Residual Mass (CRM) were used as follow: [ ( ) ( ) ] ( ) ( ) [ ] ( ) where, = predicted or simulated value; = observed value; = average observed value and = number of data used for evaluation. For a perfect model, the value of ME is 1.0, i.e., when the simulated values match perfectly with the observed ones. A lower value (close to zero) of ME indicates poor performance of the model and a negative value indicates that the modelsimulated values are worse than simply using observed mean. CRM indicates the overall under- or over-estimation of the observed value. For a perfect model, the value of CRM is zero. A positive value of CRM indicates the tendency of the model to under-estimate, whereas a negative value indicates a tendency to over-estimate the observed data Calibration and validation of WinSRM In the present study, the WinSRM model was calibrated using observed data for the Nuranang river basin. The baseflow was separated from the observed discharge data following procedure outlined in Hughes et al. (2003) and Welderufael and Data and Methodology 73

40 Woyessa (2010). Model calibration is an important part of any modelling study. Keeping this in the mind, due care has been taken while calibrating and validating the WinSRM model. Critical temperature (T CRIT ), time lag (L), runoff coefficient for snow (c S ), runoff coefficient for rain (c R ), and rainfall contribution area (RCA) are the parameters which required calibration for the present study. Calibration of model was done for snow ablation period of three years viz., 2006, 2007, and During selection of calibration period and years, certain limitation were found and listed below: i) For 2010 and 2011, though satellite images were available but continuous hydro-meteorological data were not available during snow depletion periods. ii) Selecting calibration year was not easy because of missing/absent data. Among 10 years data collected from CWC meteorological/gauging site, may be one year was found with good temperature data but discontinued discharge or precipitation data or vice-versa. iii) CWC has shut down the observatory at Jang from May 2011, so no data were available after that date. iv) Since the present study area is at high elevation, so getting cloud free satellite image is also one major problem. Images during winter season are usually cloud free but March to May/June images are mostly cloudy. Considering the above facts, five snow years (2006, 2007, 2009, 2010, and 2011) were selected with cloud free images. Out of these five, three calibration years (2006, 2007, and 2009) having all required input data to run the model were taken. These years were found appropriate to be used for calibrating the WinSRM model. On other side, validation of WinSRM model was performed for the year For this year, though the required temperature, precipitation and discharge data were available but satellite images for snow depletion period were not. So, the snow depletion curve for the year 2004 was derived using a logarithmic relationship between average mean air temperature (AMAT) and SCA% as described in Art Data and Methodology 74

41 3.5.7 Generation of snow cover depletion curve for validation year The SCA% for depletion period of validation year 2004 on monthly basis were estimated using an logarithmic relationship between monthly average SCA% and monthly AMAT of three calibration years. The logarithmic relationship between SCA and AMAT is described as below: ( ) ( ) where, SCA = Snow covered area in percentage and = mean air temperature ( C) and a coef & b coef are the coefficients to be determined. The average SCA% for each zone including whole basin and average monthly temperature for depletion periods of three calibration years (2006, 2007, and 2009) are presented in Table The logarithm equation (Eqn. 3.20) was derived for each zone including whole basin (Fig. 3.19) and the coefficients (a coef and b coef ) for each zone and whole basin were also calculated. The values of coefficients with r 2 are given in Table Using these coefficients, SCA% was estimated for each zone of validation year 2004 from observed monthly average temperature. Daily SCA% was determined by interpolating monthly values of SCA (Fig. 3.20) Validation of generated snow cover depletion curve for 2004 The SCA% estimated by the logarithmic relationship was validated by comparing estimated monthly SCA% and observed snow covered data from MODIS for year MODIS fractional snow cover product for year 2004 of snow depletion period (February June) was downloaded from ( After image processing, the snow cover percentage along with corresponding cloud cover percentage were determined for the depletion period of year For each month, least cloud cover percentage was considered for the present study and their corresponding snow cover percentage was taken for comparing with the estimated SCA% from logarithmic relationship. Data and Methodology 75

42 Average SCA (%) Table 3.10 Monthly average SCA% and average temperature from all three calibration years Month Avg. SCA(%) (Zone 1) Avg. SCA(%) (Zone 2) Avg. SCA(%) (Zone 3) Avg. SCA(%) (WB) Avg. Temperature ( C) February March April May June Z y = ln(x) R² = Average temperature ( C) a) Monthly average temperature vs. average SCA% for zone 1 Fig Monthly average temperature vs. average SCA% for different zones (contd.) Data and Methodology 76

43 Average SCA (%) Average SCA (%) Z y = ln(x) R² = Average temperature ( C) b) Monthly average temperature vs. average SCA% for zone Z3 y = ln(x) R² = Average temperature ( C) c) Monthly average temperature vs. average SCA% for zone 3 Fig Monthly average temperature vs. average SCA% for different zones (contd.) Data and Methodology 77

44 Average SCA (%) WB y = ln(x) R² = Average temperature ( C) d) Monthly average temperature vs. average SCA% for whole basin Fig Monthly average temperature vs. SCA% for different zones (concld.) Table 3.11 Coefficients (a coef and b coef ) and r 2 values Elevation zones a coef b coef r 2 Zone Zone Zone WB Data and Methodology 78

45 Snow cover area (%) WB Z1 Z2 Z3 Date (DD-MM) Fig Generated depletion curves for the validation year (2004) Data and Methodology 79

46 From the validation result, the r 2 was obtained as 0.9 and results were shown in Table 3.12 and Fig The result indicated that the estimated SCA% by logarithmic relationship performs satisfactory and can be used for the estimation of SCA% under unavailability of satellite images for similar kind of basin having observed temperature data. 3.6 Development of climate affected depletion curve for the selected hydrological year The year 2007 was selected to study the effect of climate change under different projected scenarios on snow depletion curve. The monthly average temperature variation of 2007 matched well with the long term average temperature data recorded at CWC, Jang. In 2007, the lowest temperature was found in the month of February which is same as in case of long term average data, and again in the same month the snow cover extent was maximum (almost 100% coverage of the entire basin). Thus it was decided that 2007 would be the selected hydrological year for climate change study Rectification of temperature and precipitation data The temperature and precipitation data for the selected hydrological year i.e., 2007 were rectified to represent the present climatic condition by eliminating the impact of yearly fluctuation of temperature and precipitation on the snow cover depletion (Martinec et al., 2008). The deviations of monthly average temperature of the selected hydrological year (2007) from long term monthly average temperature for were determined by the following formula: ( ) ( ) where, = Mean monthly temperature of 2007 and ( ) = long term average for Data and Methodology 80

47 Snow covered area (%) Table 3.12 Comparison between MODIS snow cover product and estimated SCA% from logarithmic relationship Month MODIS SCA% Estimated SCA% Feb Mar Apr May Jun r MODIS Logarithm Feb Mar Apr May Jun Month Fig Plot of MODIS vs. estimated SCA% from logarithmic relationship Data and Methodology 81

48 Then, the daily temperatures of the selected hydrological year were rectified/adjusted with monthly fluctuations preserved. ( ) where, T year = Daily temperature of year Ratios of long term monthly mean precipitation for to the monthly mean precipitation of the selected hydrological year (2007) was determined using following equation: ( ) ( ) where, = Mean monthly precipitation of 2007 and ( ) = Long term monthly mean precipitation for The rectified precipitation value was obtained by adjusting the daily precipitation of the selected hydrological year (2007) with monthly fluctuations preserved. ( ) where, = Daily precipitation data for the year 2007 Snowmelt depth was obtained by simply multiplying the rectified temperature with degree-day factor (a) as below: ( ) where, a = Degree-day factor, cm C -1 When the temperature at a particular day in the depletion period is lower than critical temperature, the rectified precipitation for the corresponding day will be Data and Methodology 82

49 considered as new snow and it contributes to snow melt depth for the next day when rectified temperature exceeds critical temperature Generation of conventional depletion curve (CDC) Monthly SCA% was obtained from periodical snow cover mapping for the selected hydrological year By interpolating monthly SCA%, CDC for selected hydrological year was generated by plotting daily snow covered percentage with respect to corresponding days. Depletion curves indicate the snow coverage on each day of the melt season. They have been also used as indicator of snow reserves and water equivalent. But the decline of snow cover extent not only depends on the initial snow reserves, but also on the climatic conditions of the year being considered. Therefore, a modified depletion curve has been proposed to normalize such differences between different years to represent present climatic condition. These modified depletion curves relate the SCA to the cumulative snowmelt depth computed each day based on rectified temperature and precipitation data for selected hydrological year Generation of modified depletion curves (MDC) The future course of the depletion curves of the snow coverage can be evaluated from the MDC. These curves can be derived from the CDC by replacing the time scale with cumulative daily snowmelt depth. MDC relates the SCA to the cumulative snowmelt depths. These curves enable the snow coverage to be extrapolated manually by the user several days ahead by temperature forecasts so that this input variable can be available for discharge forecasts. So, the MDC can be used to evaluate the snow reserves for seasonal runoff forecasts. MDC of the snow coverage indicate how much cumulative snowmelt depth is necessary in order to reduce the SCA to a certain percentage of the total area for the given starting snow cover. In a warmer climate, this cumulative snowmelt depth will be reached at an earlier date and the CDC of the snow coverage will be shifted accordingly. Using these climate affected CDC together with precipitation and temperatures given by a projected climate scenario, the future changed runoff can be computed. Data and Methodology 83